Colour displays for categorical images

Glasbey, C. A.; Heijden, Gerie van der; Toh, V.; Gray, Alison

doi:10.1002/col.20327

Cited by 31 publications

(28 citation statements)

References 18 publications

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“…Consequently, in either of its forms, the MS method rediscovered recently by Erişoglu et al does not appear to outperform the classical MM method or the more recent hierarchical methods VP and PP. This is not surprising given that MS can easily choose two nearby points as centers provided that they each have a large cumulative distance to the remaining centers [42].…”

Section: Resultsmentioning

confidence: 97%

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Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Çelebi

Kingravi

2014

Partitional Clustering Algorithms

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Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erişoglu et al. performs surprisingly poorly.

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Section: Resultsmentioning

confidence: 97%

“…Therefore, this method was not included in the experiments. More recently, Glasbey et al [42] mentioned a very similar method within the context of color palette design.…”

Section: Linear Deterministic and Order-invariant K-means Initializmentioning

confidence: 99%

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Çelebi

Kingravi

2014

Partitional Clustering Algorithms

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“…To explore segmentations, the MMB provides adjustable lookup tables (Glasbey et al, 2007;Hanslovsky et al, 2020) to overlay each segment with a distinct colour onto the image data. We use this functionality to visualise nuclei, tissue and cell segmentations on top of the image data in the PlatyBrowser (Figure 7B,D).…”

Section: Platybrowser: a Multimodal Browser For Platynereis Dumeriliimentioning

confidence: 99%

Whole-body integration of gene expression and single-cell morphology

Vergara

Pape

Meechan

et al. 2020

Preprint

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Animal bodies are composed of hundreds of cell types that differ in location, morphology, cytoarchitecture, and physiology. This is reflected by cell type-specific transcription factors and downstream effector genes implementing functional specialisation. Here, we establish and explore the link between cell type-specific gene expression and subcellular morphology for the entire body of the marine annelid Platynereis dumerilii. For this, we registered a whole-body cellular expression atlas to a high-resolution electron microscopy dataset, automatically segmented all cell somata and nuclei, and clustered the cells according to gene expression or morphological parameters. We show that collective gene expression most efficiently identifies spatially coherent groups of cells that match anatomical boundaries, which indicates that combinations of regionally expressed transcription factors specify tissue identity. We provide an integrated browser as a Fiji plugin to readily explore, analyse and visualise multimodal datasets with remote ondemand access to all available datasets. Figure 1: Ultrastructure of a whole Platynereis by serial block-face scanning electron microscopy.A: The 3D SBEM dataset can be observed in multiple orientations, either along the acquisition plane (transversal plane, top row) or as orthogonal projections (horizontal plane, bottom row; scale bar: 50 µm). B-E: fine ultrastructure is revealed when exploring the datasets at native resolution (10 nm pixel-size x/y; scale bars: 2 µm). B: epithelial cell, interfacing the cuticle and the underlying muscle. Bundles of cytoskeletal filaments (arrowhead) form part of the attachment complex (inset). C: the adult eye forms a pigment cup composed of pigment cells (PiC) and rhabdomeric photoreceptors (rPRC). The photoreceptors extend a distal segment made by microvillar projections (mi) for light detection. In the centre of the pigment cup is the vitreous body (vb). D: longitudinal muscle fibres are cut transversally displaying cross-sections of the sarcomere as well as of the sarcoplasmic reticulum that contacts the plasma membrane (inset). E: cross section of the distal part of the nephridia, highlighting the autocell junction (arrow) which forms a lumen. The lumen houses a bundle of motile cilia (identified by the 9+2 microtubule arrangement, inset), which are contributed by each cell of the nephridium, noted by the presence of basal bodies. Panels B to E are snapshots selected from the full volume and can be retrieved directly from the PlatyBrowser "Bookmark" function. Supplementary Figure 2A). Figure 2 gives a few examples of the achieved segmentation quality for epidermal cells (B), muscles (C) and nephridia (D). We measured nuclei sizes to range from 33.6 to 147.5 cubic microns, and cell sizes from 59.8 to 1224.6 cubic microns. Note that neurites in the neuropil have not been segmented, as they are not sufficiently preserved in the EM volume for automated segmentation. Figure 2: Segmentation of nuclei, cells, tissues and body parts.A. Cells and nuclei ar...

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“…Campadelli et al in [14] determined the task of nominal coding as a combinatorial optimization problem on graphs and used Hopfield neural network to find aproximate solutions. Glasbey et al in [8] compared a simulated annealing and greedy algorithm methods to display the most conspicuous colors of specific size. Carter and Huertas in [15] studied the methods for displaying small visual targets for ultra-large color difference using several color difference metrics and color spaces.…”

Section: Introductionmentioning

confidence: 99%

Visualization enhancement of segmented images using genetic algorithm

Radlak

Smołka

2014

2014 International Conference on Multimedia Computing and Systems (ICMCS)

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The aim of the paper is to present the results of investigations concerning the implementation of pseudocolor visualization algorithm of segmented images, capable to find the color combination producing maximum contrast between the segmented areas. Very often there is a need of visualization of segmentation results and usually they are presented by assigning colors randomly or from predefined palettes, what could decrease the visualization effect, when neighboring regions have assigned similar colors. To alleviate this problem, we propose novel methodology for deriving optimized visualization based on maximizing local distance between colors. In the paper we present visualization results using a new color contrast measure optimized with a genetic algorithm and compare the effectiveness with a greedy algorithm. The proposed method can be used to obtain visually pleasing pseudocolor encoded images of segmentation results which can be useful for the presentation of various kinds of visual information.

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Colour displays for categorical images

Cited by 31 publications

References 18 publications

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Whole-body integration of gene expression and single-cell morphology

Visualization enhancement of segmented images using genetic algorithm

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